GTM Engineering

AI Agents and AI SDRs

Everett Berry

Head of GTM Engineering at Clay

Understand what agents are and how they are being used in GTM. Enumerate use cases and tactical reasons for using agents

Agents in GTM Introduction

Learn why outbound is an attractive target for agents, pitfalls, and current state. Explore the black box approach.

AI SDRs

Explore the human in the loop approach and what connectors and systems are needed for effective agents.

Approaches for Building Agents

We’re living through one of the most transformative periods in the history of computing — and Generative AI is at the heart of it. From content creation and personalized customer support to autonomous decision-making and intelligent search, GenAI is rapidly redefining how businesses operate, how knowledge is accessed, and how software is built. This course is roughly divided in two parts: 1. No Code tools for building AI Agents2. Low code approaches for building more complex Agents You've likely experienced this firsthand: vendors promising AI solutions that will transform your business overnight, countless products with overlapping capabilities, and the nagging question: "Which of these actually solves our unique problems?" Our startup has worked with organizations across fintech, supply chain, consulting, and manufacturing sectors, and we've consistently encountered the same challenge: businesses know AI matters, but struggle to identify concrete use cases that deliver measurable ROI. This course addresses precisely that gap—helping you cut through the noise to identify, build, and deploy GenAI applications that create actual business value. Building GenAI Agents for Enterprise Use is designed to give you a laser-focused and in-depth view of real-world GenAI applications. Designed for Business Leaders, Not DevelopersWhat sets this course apart is our relentless focus on the "what" rather than just the "how." While many courses dive directly into technical implementation, we prioritize use case identification, business value assessment, and strategic deployment considerations that matter to non-technical leaders. You don't need a programming background to succeed here. For business professionals who want to understand the capabilities without getting lost in the code, we've designed an experience that emphasizes concepts, applications, and outcomes. This course is one of the top-rated Gen AI Course on Maven, with over 1000 professionals trained from companies across tech, finance, government, and healthcare. It has been successfully offered at Stanford Continuing Studies and the UCLA Anderson School of Management for their Master’s in Business Analytics programs. Designed for Executive leaders, Data Scientists, Machine learning engineers, and Product Managers, the course demystifies the Gen AI landscape by blending deep technical concepts with practical enterprise deployment strategies. This is not another LangChain how-to or framework tour. Instead, this course teaches you to build modern GenAI systems from first principles, grounded in real-world constraints like scale, guardrails, multi-agent workflows, and domain-specific adaptation. 👤 Who This Course is For This course is designed for: - Product Managers looking to integrate GenAI into their roadmaps with clarity and confidence - Technical Leaders and Architects who want to build scalable and safe GenAI systems - Startup Founders &amp; Innovation Teams aiming to create competitive GenAI-powered products - Data Scientists &amp; ML Engineers who want to go beyond notebooks and deploy impactful GenAI apps - Consultants &amp; Enterprise Strategists exploring GenAI adoption and implementation strategies for clientsAnyone who wants to learn how Gen AI can be used 📚 Course ModulesModule 1: The World of Generative AI — Why Leaders Must Understand ItUnderstand the GenAI revolution and what it means for industries, infrastructure, teams, and the future of software. We break down the LLM ecosystem, the commoditization of models, and the real moats: data and architecture. Module 2: Building Blocks of no Code AgentsWhy do most enterprise search and Agentic solutions disappoint users? Discover the fundamental components that transform basic document search into contextual intelligence systems. Get hands-on with embeddings, chunking strategies, and the architecture of RAG systems that actually return relevant information. Connect RAG to Agents to interact with end users Module 3: Enterprise RAG with Guardrails—Moving Beyond PrototypesWhy do systems that work in demos fail in production? Learn how to transform proof-of-concepts into enterprise-grade solutions with semantic caching, optimized inference, and essential safety controls. Implement trustworthy systems that match your organization's risk tolerance and compliance requirements. Module 4: Agents and Their Ecosystem — Vertical AI AgentsWhat are agents really? Go beyond CrewAI and Autogen to build your own agent orchestration framework using AgentPro. Focus on vertical agents like Data Analyst Bots, Research Assistants, and more. Module 5: Teaching LLMs Your Organization's Language Through Fine-TuningWhy do generalist models struggle with your industry's terminology? Learn practical approaches to adapting models to your domain's vocabulary, processes, and knowledge structures. Implement LoRA fine-tuning and knowledge graph integration techniques that ground AI in your organizational context. Module 6: Capstone Discussion WeekWorkshop your final project ideas, get peer and instructor feedback, and finalize your architecture and approach for your custom RAG or Agent system. Module 7: Demo DayPresent your fully built, working system to the cohort. From agent demos to enterprise RAG deployments — this is where theory meets execution. Why This Course Is Different?The GenAI landscape is cluttered with tools, frameworks, and platforms—each promising to be the silver bullet. Our approach isn't about teaching you specific tools that will be outdated in six months. Instead, we focus on the enduring principles of identifying high-value use cases and building solutions that address actual business needs. Our startup has guided organizations through the process of moving from AI curiosity to deployment, and we've distilled those lessons into this course. You'll learn to look beyond the marketing hype to determine which problems in your organization are genuinely suited for Gen AI solutions. This Course Delivers Results Where Others Don'tUnlike theoretical courses that leave you wondering how to apply concepts, or technical deep-dives that ignore business realities, this program bridges the gap between possibility and practicality.We've designed every session to answer the question: "How do I use this to create actual value in my organization?" Whether you're looking to enhance customer experiences, streamline operations, or unlock insights from unstructured data, you'll complete this course with concrete tools to transform GenAI from a buzzword into a business advantage. ✅ What You'll Get (Outcomes, Action-Oriented) By the end of this course, you’ll be able to: 🧠 Understand the GenAI Landscape: Cut through the noise and get a clear grasp of how LLMs, RAG, and Agents fit into real-world enterprise workflows. 🛠️ Build Production-Ready AI Agents: Learn how to design and deploy AI Agents that go beyond simple Python scripts — handling reasoning, memory, retrieval, and tool use. 💼 Deliver Business Impact: Map Agent use-cases to measurable ROI — from automating internal knowledge retrieval to customer service, market analysis, and more. 🧪 Customize &amp; Scale Your Solutions: Fine-tune domain-specific models, design feedback loops, and deploy with guardrails. 🚀 Capstone + Demo Day: Work on a real-world Agent project and showcase it to a private network of peers, builders, and mentors.

What can Agents do for me?

Course syllabus

If you haven't written code before, no worries at all. We will provide you with everything you need to get started.

Just be willing to learn, we will take of the rest

We will go through each module during this class

Wednesday: Module Teaching

Students will spend time building projects with their team members or individually

Weekly projects

Course schedule

Product Managers looking to integrate GenAI into their roadmaps with clarity and confidenceYou want to cut through the jargons and actually

Technical Leaders and Architects who want to build scalable and safe GenAI systems

Startup Founders &amp; Innovation Teams aiming to create competitive GenAI-powered products

Data Scientists &amp; ML Engineers who want to go beyond notebooks and deploy impactful GenAI apps

Who is this course for

Actionable Business Value, Not Just Technical Knowledge Outcome

Develop a structured approach to evaluate which business problems in your organization are genuinely suited for GenAI solutions. Stop wasting resources on use cases that look good in demos but fail to deliver ROI.

A Framework for AI Use Case Identification

Create actual, functioning GenAI applications using our provided code templates and step-by-step guidance. You'll build and deploy real systems—not just learn concepts.

Practical Experience Building Working Systems

Connect with professionals facing similar challenges across diverse sectors. Previous cohorts have formed lasting relationships that continue to provide value long after the course ends.

A Network of Cross-Industry Peers

Develop the ability to assess AI vendor claims and capabilities objectively. Learn which questions to ask and what metrics actually matter when evaluating solutions.

Vendor-Neutral Evaluation Skills

Build a clear, phased approach to GenAI implementation for your organization that aligns with business objectives and accounts for technical constraints.

Strategic Implementation Roadmap

Master the what, why, and how of Generative AI by building Enterprise-Grade Agents that go beyond the basics and actually move the needle

Hamza Farooq

Founder | Ex-Google |
Instructor Stanford Continuing Studies & Adjunct UCLA

2025-01

2025-02

If you are a Product, Product Marketing, UX, or Engineering leader trying to make sense of AI agents, beyond the hype, jargon and noise of the demos, this course is for you. Most teams don’t struggle with the technology of AI agents; they struggle with knowing when to use them, how to justify them, and how to deliver business value with them. This course helps you master the product and business strategy of AI agents - no coding, no technical deep dives. Just practical, commercially-minded frameworks that make you a more credible and confident AI Agent leader. In this course, you will learn to: Learn when, why, and how to use AI agents to solve real business problems - not just chase hype. You’ll gain the judgment to distinguish opportunities from distractions. Master the difference between agents and automation, so you can choose the right solutions, justify it clearly, and avoid costly missteps. Use the Agent Strategy Canvas, a proven framework to define goals, align stakeholders, and scope Agentic initiatives that are commercially viable. Navigate build-buy-partner decisions with confidence, evaluating trade-offs, vendor fit, and organizational readiness through a structured lens. Lead the AI Agent governance discussion by utilizing a cutting edge framework and setting the tone for AI governance within your company. Understand the full cost of ownership beyond tech and tools - to include change management, operational scaling, and long-term sustainability and performance. Deliver a compelling capstone project: a real-world business case that showcases your strategic thinking and positions you as an AI-savvy leader. There will be feedback from real-world business leaders reviewing your presentation. This is not a technical course. We will not cover agent architecture, comparing no-code Agent tools, RAG, fine-tuning, or coding. Instead, we focus on strategy, decision-making, and business impact. This course is a "bootcamp" style format. We meet 4X/week and there will be homework everyday. To get the most of this course, please ensure you have adequate time to complete your work everyday.

Become a Top 1% business-savvy AI Agent PM, UX or Engineering leader 

Week 1: Tuesday to Friday

We will meet 4X/week again. Week 2 includes Office Hours and Capstone project.

Week 2: Monday to Thursday

Unlike simulated cases, you will work on live cases from Industry leaders and get real feedback from them directly.

Capstone project

Product Management &amp; Marketing Leaders.Build commercial fluency to scope, justify, and lead Agent strategies that drive real business value

UX and Engineering leaders. Think beyond execution. Gain a strategic view of Agents to become a stronger, cross-functional product partner.

Entrepreneurs, tech founders. Craft board-ready Agent strategies that align with business models, value creation, and go-to-market clarity.

What you’ll get out of this course

Agents are unique digital products that are closest to thinking like a human. Understand how they work.

Deeply understand the nature of AI agents. 

Distinguish between AI Agents and traditional automation to make the right investment. We will play a fun game with various live cases to improve our decisions regarding this critical topic.

Identify when to use Agents vs. Automate. 

This Agent Strategy Canvas is a proprietary framework developed from my time at Wayfound.ai to help clients evaluate Agent strategy holistically. It is a structured framework to define goals, capabilities, and align agent initiatives to business outcomes and customer innovation.

Apply the Agent Strategy Canvas
 

With the plethora of options available to create Agents today, what is the best decision for your company? Assess internal capabilities vs. external options and make informed decisions to build, buy, or partner.

Drive Build-Buy-Partner Decisions
 

Evaluate ROI, Total Cost of Ownership, and strategic fit to justify AI Agents to your executive stakeholders and your board.

Build a Business Case for AI Agents. 
 

Deliver a capstone project that simulates a real-world agent proposal. Senior leaders from F100 and start-ups will be there for live sharing and feedback.

Become Board-Ready and more confident 
 

Learn commercial frameworks for strategy, build-buy-partner, governance and cost. Lead AI agent decisions with clarity and credibility

Harvard Business Review

CNBC

KTVU

Subha Shetty

Chief AI Officer at Wayfound.ai, Former VP of Product @ Fanatics

July 2025 cohort 

September 2025 cohort 

Like many software people, I started out as a developer before moving into product and executive leadership, but I never lost my love for the game. After years of barely coding, AI has given me the ability to build again, and it's also introduced a new concept -- agentic workflows -- which opens the door to truly intelligent software. I've been building AI workflows and writing AI code for almost 2 years now, and I can say two things with extreme confidence: 1) AI tools are going to completely change how we build software, both in terms of code and architecture, and 2) you can't learn how to do it from a book -- you need to get your hands dirty. This one-day, immersive workshop is designed for developers who want to quickly build competence with agentic coding and workflow tools. Former developers, engineering managers, technical PMs or designers, etc. are all welcome -- if you sorta know how to code or spend a lot of time close to code, you'll get a ton out of this course. I will show you how I use AI tools to code and build flows in the real world, we'll work through examples together in your own local environment, and you'll end the day having built real software with AI. No sales pitches, no bullshit -- just real stuff you can use that will get your creative juices flowing. WHAT YOU CAN EXPECT Hands-On ExamplesWe'll start with a few basic concepts around autonomy and then look at a variety of tools -- from Copilot to Claude Code to Cline, and everything in between -- and I'll share real client stories of using agentic AI to build and deploy software and agent workflows with LangGraph and LangSmith. We'll also look briefly at low-code builders like Lovable, Bolt and Tempo. Then we'll learn by doing, applying those tools and techniques to real codebases (we'll cover frontend React, backend Java, and LangGraph Python workflows) in your local dev env. Curated Tools and Techniques I'll demonstrate and help you get set up, answer any and all questions in real time, and share tips and techniques that have worked for me and help you adapt them to your process. You won't write a single line of code yourself, but you'll have several working software examples by the end of the day. I've done this with large teams (see Testimonials below) and it's amazing how much you can get done when AI is in play! Rapid Progress + Community and SupportYou may wonder why this isn't a multi-day (or multi-week) course -- but the goal of this workshop is to kickstart your ability to use AI in a software context. What you do with that ability is up to you -- you can build awesome prototypes, or become a model behavior architect designing and tuning workflows (much like I'm doing today), or be a superfast full stack app builder for hire. You'll be connected with the Not Safe for LinkedIn community (this is a side project of mine -- a place to discuss the changing landscape of life and work away from prying corporate eyes!) where you can reach me and other workshop folks as your journey unfolds. I'm always available for questions and advice -- and for coaching and private workshops if needed! Enhance Your ExpertiseVibe coders aside, most people who are winning with AI are leveraging existing expertise to move faster and make better choices. Lean into what you're already good at, use AI to make it even better, and you'll have a leg up on the competition. This applies to all software roles -- product, dev, design, project mgmt, etc. Immediate, Tangible ROIUsing AI can make you faster and better - which either frees up time to do more of what you love, or increases the value you deliver per unit of time, so you can deliver more value (and hopefully get paid for it!). At worst, you'll look awesome to your boss. --- Agentic coding and workflows are going to completely change how software is built. Don't miss out -- get in the game, get good at this stuff, and build a better future for you and your teams! Please note: Private cohorts are available for teams of 50+! ﻿Please reach out to me at d@jpsj.me if you're interested in a customized version of this workshop for your engineering and/or product delivery team. We can focus on technologies native to your stack and/or agentic tools that are available in your environment (Copilot, Cursor, Claude Code, Codex, etc.)

One-day hands-on AI coding workshop for developers -- from FOMO to FAFO, fast!

Make sure you have 8GB of RAM, admin mode and Homebrew or Chocolatey or similar to help you get going. Replit also works!

A development environment

Options include VSCode with GitHub Copilot, Cursor, Windsurf, Replit, Cline, Claude Code -- free Copilot may work, but be ready to subscribe

Access to an agentic coding tool

Lapsed coders will be fine, non-coders are still more than welcome, but if you don't know what code looks like, it may be tough to keep up

You need to be comfortable looking at/working with code

We'll introduce agentic coding and workflow concepts, and show-and-tell a variety of tools, examples and best practices.

Part 1: Concepts/Demos

Break

We'll do a live workshop, hands on keyboard, where we all use AI tools to modify and run sample code exercises. You'll leave with working software examples that you can continue to modify and enhance on your own.

Part 2: Hands on Keyboard Workshop

Engineers who are already coding with AI, or who could use a push to get started, and want to be thoughtful about best practices and tooling

Product managers or designers who want to level up technically and start building prototypes or workflows without developer help

Managers and executives who want to understand what's possible with state of the art tools, and maybe even get back into the game themselves

We'll work through real examples of frontend and backend code, and use AI tools to modify it. You won't write a single line of code yourself!

You'll use AI codegen tools to build and modify real software

We'll discuss opportunities and pitfalls when using AI to build software, and ensure that you can bring this knowledge back to your teams

You'll use and understand best practices for AI coding in production environments

We'll explore tools like Lovable, Tempo, GitHub Spark and more, and understand how they can be integrated into a larger build process if your prototype(s) get the green light

You'll explore AI rapid prototypers and how to shape their output

We'll use LangGraph (one of many agent frameworks) to create workflows, run them locally and observe chain of thought and tool use. Please note: this requires an API key -- Claude, OpenAI, Gemini, OpenRouter all fine

You'll build and run agentic workflows, from start to finish

One day, no gimmicks, no pitches, just building real code and workflows.  You'll walk away competent and confident in building with AI

Dan Mason

Principal, Head of AI @ Stride

Cohort 1

Note: This course is an independent offering and is not affiliated with, endorsed by, or related to the instructor's past employers. If you work in an enterprise, this is the course for you.AI agents are powering intelligent applications, automating tasks, reasoning over data, and working together to solve complex problems. Many builders find it hard to go beyond simple demos and build systems that are ready for production. You may have used frameworks like LangChain or tools such as CrewAI, but chaining multiple agents, managing shared memory, orchestrating tools, and meeting performance targets in a live environment can still feel overwhelming. This course uses a problem-driven approach to show you how to build, test, and deploy Enterprise AI Agents and multi-agent Retrieval-Augmented Generation (RAG) pipelines. You will work with code at every step. At each milestone, you will see clear progress metrics, complete hands-on assignments, and receive real-time feedback. While you will mostly use Python to work with these tools, you'll do it in easy-to-run notebooks with step-by-step execution. This means you can see what’s happening instantly, without setting up complex environments or writing large scripts from scratch. 🏆 Why This Course Stands Out: 1. Enterprise-Ready Design PrinciplesEverything you build in this course is grounded in real-world enterprise requirements. You'll learn how to structure agent workflows for long-term scalability, fault tolerance, and ease of maintenance. 2. Context Engineering for AI Agents Master the art of context design to ensure your agents perform optimally across tasks. You’ll learn how to structure prompts, manage conversation state, and layer contextual data so agents can reason, plan, and act with minimal ambiguity. 3. Pre-Built Python Notebooks for Every Module Save time and focus on learning with ready-to-use Python notebooks for every lesson. Each notebook contains structured examples, helper functions, and starter code you can run instantly — no need to set up from scratch.&nbsp; 4. Structured Timeline and MilestonesWe know poor pacing and unclear timelines can derail your learning. Every week has clearly defined start and stop dates, deliverables, and checkpoints. At the start of each module you will see exactly which assignments are due and what you should be able to demonstrate by the end. Progress tracking is built into the portal so you never lose sight of deadlines or miss a key deliverable. 5. Build and Deploy Multi-Agent RAG SystemsThis course is designed for engineers who want to go beyond demos and learn how to build robust, production-minded Retrieval-Augmented Generation (RAG) systems using multiple AI agents. By the end, you’ll be able to design and deploy scalable multi-agent systems optimized for real-world performance. 6. Hands-On, Production-Focused AssignmentsTheory alone is not enough. Every lesson has a coding exercise with practical requirements, like reducing latency, cutting token costs, dealing with tool errors, and passing data between agents. You will not just copy paste example code; you will adapt and extend it to fit your own use cases. By week four you will deploy a multi-agent system that ingests data, runs planning flows, and outputs final results to a live endpoint. 7. Weekly Live Code Review ClinicsEvery week, you'll participate in office hours dedicated to reviewing your agent designs and implementation strategies. Instead of generic Q&amp;A sessions, you will bring your code and current blockers to weekly live clinics. The instructor and community will review your design choices, spot logic errors, and suggest concrete improvements to your architecture. These sessions keep you accountable and help you iterate quickly. 8. Easy, Step-by-Step Python Notebook ExecutionWhile this course uses Python, you won’t need to be a full-time developer. All exercises are provided in prebuilt Python notebooks that run one step at a time. You’ll see what each agent is doing instantly, making the experience interactive and beginner-friendly — even for those who haven't worked in traditional dev environments. 9. Guest Lectures from Industry PractitionersEach week you will hear from engineers and architects who have built AI agent services at scale. They will walk you through real deployment stories, show you the metrics they tracked, and share how they overcame challenges in multi-agent coordination, Error handling, and cost optimization. 💻 What You’ll Build: 1. Design scalable agent systems that coordinate tasks, share memory, and operate in real-time environments.2. Implement multi-agent RAG pipelines with persistent memory, tool orchestration, and modular planning flows.3. Build an enterprise-ready agentic system from scratch, applying production-grade design patterns including observability, retry mechanisms, and multi-agent orchestration that can scale in business-critical environments.4. Deploy your system using real-world metrics. ⚙️ Tools You’ll Use﻿1. LangChain and LangGraph for defining agent logic, managing memory state, and orchestrating complex interactions with tools via ReAct and state machines.2. CrewAI for coordinating multi-agent setups through role assignment, task planning, and collaborative workflows.3. Agent Development Kit (ADK) for mapping agents to business-grade use cases using composable modules and evaluation-driven design. ✅ Who This Course Is For: 1. Technical Architects and Data ScientistsYou already understand data pipelines and model basics. You want to learn how to integrate language models into agent workflows and design systems that use tools, maintain memory, and plan across tasks. 2. Product Managers and StrategistsYou bridge business goals with technical execution. You want to learn how to translate product requirements into AI agent capabilities, define success metrics, and collaborate effectively with technical teams to deliver scalable, user-focused agent-driven solutions. 3. Tech Leads and Backend DevelopersYou need to coordinate multiple services and agents in your organization. You want to learn best practices for orchestration, monitoring, and scaling multi-agent workflows in real business scenarios. 4. Technical ConsultantsYou advise clients on AI roadmaps or make decisions about AI investments. You need a deep understanding of how agentic systems work in practice, beyond high-level strategy or prompting tips. ❌ Who This Course Is Not For: 1. Absolute Beginners in CodingIf you are new to Python or general software development, start with a solid programming course first. This course moves quickly and assumes familiarity with writing, debugging, and testing code. 2. You are a Researcher in Model Building and TheoryThis is a hands-on, practical course that uses existing language models rather than teaching transformer internals, fine-tuning, or building models from scratch. 3. No-Code EnthusiastsThis course will work directly with code, APIs, and orchestration libraries rather than using visual, low-code platforms. If you want to build applications without writing code, you may find this course more technical than expected.

Design and deploy advanced AI agents that are ready for production

Comfort with writing, debugging, and testing Python code. You will clone repositories, install packages, and build modules from scratch.

Python Proficiency

Familiarity with how large language models work and common API patterns (e.g., sending prompts, handling responses).

Basic Concepts of Language Models

Understanding of core software engineering principles such as modularity, version control (Git), unit testing, and debugging.

Software Engineering Principles

Deep-dive classes on core concepts, architectural patterns, and design trade-offs. Includes walkthroughs of example code and interactive Q&amp;A.

Weekly Live Sessions - Part 1

Hands-on coding workshops where you implement new features in your agent system. You will work through labs that build directly on theory.

Weekly Live Sessions Part 2

Drop in to get unstuck on assignments, debug code, or ask architecture questions. This is an open forum for troubleshooting and mentoring.

Office Hours (Optional)

Get Industry Insights from Industry experts about AI Agent development, Observability and much more.

Expert Sessions

Assignments are designed to reflect real-world enterprise challenges like multi-agent coordination, fault tolerance, and system benchmarking.

Weekly Projects

Build and deploy a complete multi-agent system designed to meet enterprise-grade requirements. Your final project will simulate a real business scenario with performance benchmarks and modular agent flows.

Capstone Project

Technical Architects and Data Scientists who want to integrate LLMs into agent workflows using memory, tools, and task planning logic.

Product Managers and AI Solution Strategists translating business needs into AI agent workflows, metrics, and scalable solutions.

Tech Leads and Backend Developers managing multiple agents and services looking to scale, monitor, and orchestrate workflows.

Technical Consultants and Strategists advising on AI who need a practical grasp of agent workflows and deployment.

Access production-ready, framework-agnostic starter repositories designed to help you hit the ground running. These kits include boilerplate code and modular components to accelerate development, whether you prefer LangChain, CrewAI, or other frameworks.

GitHub Starter Kits

Work with prebuilt Python notebooks that let you run code one cell at a time. No complex setup needed—just click and execute to see how agents work in real-time, even if you’re not a full-time developer.

Easy, Step-by-Step Python Notebook Execution

Learn how to reason about coordination, memory, and planning in agent-based systems. Develop a structured approach to designing scalable multi-agent workflows using real-world engineering patterns.

Agentic System Design Mindset

Build advanced Retrieval Augmented Generation systems with multiple agents. Learn how to manage shared memory, orchestrate planning steps, and reduce latency or token costs in production-like scenarios.

Learn How to build Scalable Agentic RAG and Multi-Agent Architecture

Get personal feedback from Rakesh and your peers on your code, system design, and deployment strategy. These sessions keep you accountable and help you iterate faster.

Weekly Live Code Review Clinics

Gain insights from engineers and architects who have launched enterprise-grade agent systems. Learn their best practices for tooling, scaling, and monitoring.

Guest Lectures from Industry Experts

Build and deploy a complete multi-agent system by the end of the course. Your project will serve as a showcase of your ability to design, implement, and scale real-world agent-based AI applications.

Design, deploy, and scale enterprise-grade AI agents with a hands-on, problem-driven approach for engineers and tech leaders.

Rakesh Gohel

AI leader with 25 years in Agentic AI, Automation, and Intelligent Workflows

Build, collaborate, and demo advanced multi-agent AI systems together.

Lightning Lessons

AI Agents and AI SDRs